Fraunhofer Institute for Wind Energy Systems
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Exposé of the field study in the research project SPELL
No full textUm Transparenz und Reproduzierbarkeit zu ermöglichen, wird der Forschungsplan zur Feldforschung im Projekt veröffentlicht. Die Feldforschung adressiert Aspekte der Aufmerksamkeitsverteilung und des Mental Workloads während der Prozesse "Notrufannahme" und "Einsatzdisposition".PDF-Fil
Data for "Machine learning-based sampling of virtual experiments within the full stress state"
No full textData for "Machine learning-based sampling of virtual experiments within the full stress state" by Alexander Wessel, Lukas Morand, Alexander Butz, Dirk Helm and Wolfram Volk.The authors gratefully acknowledge funding from the Federal Ministry for Economic Affairs and Climate Action via the German Federation of Industrial Research Associations – AiF (Arbeitsgemeinschaft industrieller Forschungsvereinigungen e.V.) within the scope of the programme for Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF), grant numbers 19707 N and 21466 N, and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 415804944
Fraunhofer-Fachinformationsmanagement in der externen Wissenschaftskommunikation
Get PDFIm Rahmen einer Masterarbeit zum Thema »Positionsbestimmung wissenschaftlicher Bibliotheken in der externen Wissenschaftskommunikation am Beispiel des Fraunhofer-Fachinformationsmanagements« wurde eine Online-Befragung durchgeführt. Sie richtete sich an Fraunhofer-Fachinformationsmanager:innen und Mitarbeitende der Kommunikationsabteilungen, um ihre Aktivitäten in und Einstellungen zur externen Wissenschaftskommunikation zu erheben. Nähere Informationen zur Durchführung und Auswertung können der im Datenbankeintrag verlinken Masterarbeit entnommen werden.An online survey was conducted as part of a master's thesis on "Positioning of scientific libraries in external science communication using the example of Fraunhofer's scientific information management". It was directed at Fraunhofer library and information services and employees of the communication departments in order to ascertain their activities in and attitudes toward science communication. Further information on the survey can be found in the master's thesis linked in the database entry.Die Daten liegen im XLSX-Format vor. Die Variablennamen und -ausprägungen wurden gemäß eines selbst erstellten Codebuchs bearbeitet, um die Analyse zu vereinfachen, da die ursprünglichen aus der Umfrage-Software systembedingt inkonsistent und nicht intuitiv waren.
Außerdem wurden Freitextantworten, die Rückschluss auf einzelne Personen zuließen, anonymisiert. Die entsprechenden Textteile wurden durch sinngemäße Alternativen ersetzt, um das Verständnis zu erhalten.The data are available in XLSX format. Variable names and values were edited according to a self-created codebook to simplify the analysis, as the original ones from the survey software were inconsistent and not intuitive due to the system.
In addition, free text responses that allowed inference to individuals were anonymized. The corresponding parts of the text were replaced by alternatives that made sense in order to maintain comprehension
Supplementary Materials for Capacitive Dosing using an Alternating Drive Mechanism and Micro Pumps
No full textRaw data of the experiments for a capacitive sensor for microfluidic applications, (capacitance sensor and scale data), videos showing a mixing process inside the channel
BioMates - Probenzuordnung - Deliverables in Arbeitspaket 1: Neuartiges Pyrolyseöl aus Biomasse ohne Futter- und Nahrungskonkurrenz
No full textIn the H2020-project BioMates (www.biomates.eu, Grant Agreement No. 727463), Fraunhofer UMSICHT produced samples from ablative fast pyrolysis of herbaceous biomass in a TRL 4-plant. A dedicated document provides identifiers for relevant liquid samples and their blends (DOI: 10.24406/fordatis/156). The document at hand maps it to the substances reported to be used in the deliverables connected to Work Package 1 “Novel pyrolysis oil from non-food/feed biomass” of the BioMates-project.Read with: Adobe Acrobat Reade
Complex refractive indices of Spiro-TTB and C60
No full textCombining spectrophotometry, variable angle spectroscopic ellipsometry, and X-ray reflectometry with an algorithm that simultaneously fits all available spectra we determine the complex refractive index of evaporated Spiro-TTB and C60 layer
Auxiliary data tables relating process parameters and parameters of the Herschel-Bulkley rheology model to a deformation profile
No full textThis data set contains two files. The first file "process-parameters-to-reach-target-shear-distribution.csv" provides a detailed set of curves in accordance with Figure 3.4 in the linked paper. Here, the first row "PhiGammaSuf" is the ordinate and all following rows are the respective abscissae for a certain pair of sufficient shear (gammaSuf) and flow exponent n. Please refer to the main text of the paper for a detailed explanation. The second file "relation-consistency-to-mean-velocity.csv" consists of the data set in accordance with Figure 3.7. Here, the first column "R-DeltaP-By-L-tau0" is the ordinate and all subsequent columns belong to a certain flow index. The headers have a format of "y-for-n={value}", where "y" is a substitution for as shown on the abscissa in Figure 3.7 and {value} is the corresponding flow index of interest. Again, please refer to the main text for a detailed explanation.The files are formated in a such a way that they can be easily opened in Microsoft Excel. Please be aware that columns are separated by commas (",") and the dot (".") is used as decimal point
Data for "Machine learning-based sampling of virtual experiments within the full stress state to identify parameters of anisotropic yield models"
No full textData for "Machine learning-based sampling of virtual experiments within the full stress state to identify parameters of anisotropic yield models" by
A. Wessel, L. Morand, A. Butz, D. Helm and W. Volk.The authors gratefully acknowledge funding from the German Ministry of Economic Affairs and Climate Action via the German Federation of Industrial Research Associations – AiF (Arbeitsgemeinschaft industrieller Forschungsvereinigungen e.V.) within the scope of the programme for Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF), grant numbers 19707 N and 21466 N, and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 415804944
In-Situ Stress Magnitude Data from the Greater Ruhr Region (Germany) Derived from Hydrofracturing Tests and Borehole Logs
No full textThis compilation presents in-situ stress magnitudes derived from hydrofracturing tests and borehole logs carried out in six, now abandoned, coal mines and two coal bed methane boreholes at depths between 600 and 1950 m within the greater Ruhr region in western Germany. All stress information was analysed and compiled in a standardized format and quality-ranked, using the established quality ranking schemes of the World Stress Map (WSM) project, for reliability and comparability. The compilation displays A-C quality of in-situ stress orientation data records. The database presented here is the world's largest public database of stress magnitudes from a single region and presents unique and high-quality stress input data for future reservoir and geomechanical numerical models and should aid the subsurface operations in the region. This dataset supplements the Stress Magnitude Database of Germany (https://doi.org/10.5880/wsm.2020.004) with additional 429 data records, doubling the amount of available stress data records for Germany from 568 to 997. More detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at http://www.world-stress-map.org.The funding of the Geothermale Papiertrocknung project (EFRE-0801837) depicting the frameworks for the elaboration of the present study, by the European Union and the Ministry for Economic Affairs, Innovation, Digitalization and Energy of the State of North Rhine-Westphalia and the Ministry of Culture and Science of the State of North Rhine-Westphalia, respectively, is greatly appreciated. The funding of the 3DRuhrMarie (“FHprofUnt2016”) project from the German Federal Ministry of Education and Research and geomecon GmbH is also acknowledged.The data is saved in .csv file format. Additional file with data description is attached. UPDATE on 30.09.2022: Corrected mistake in the geographic coordinates of a few data records (i.e., Rieth-1 borehole)
Spectrogram Data Set for Deep Learning Based RF-Frame Detection
No full textAutomated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions as well as coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis.
In this publication, we introduce a data set that can be used to train and evaluate a deep learning model, capable to detect frames of different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed.
The data set consists of 20000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The dataset contains results of intermediate processing steps that enables the research or teaching community to create new datasets for specific requirements or to provide new interesting examination examples.This work was funded by the Federal Ministry of Education and Research of the Federal Republic of Germany (BMBF) within the PENTA project “SunRISE” (https://www.project-sunrise.eu/) under the Project Number 16ES0974 and in cooperation with the Center for Analytics – Data – Applications (ADA-Center) which is supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy within the framework of “BAYERN DIGITAL II” (20-3410-2-9-8).See correspondig publication "Spectrogram Data Set for Deep Learning Based RF-Frame, Jakob Wicht, Ulf Wetzker and Dr. Vineeta Jain". Additional helper scripts are provided at https://gitlab.cc-asp.fraunhofer.de/ifk_public/sunrise/public-mdpi-dataset-helper-scripts/-/tree/dataset_2022071